Example- Schedule Optimization

  • Release version: Zurich
  • Updated July 31, 2025
  • 2 minutes to read
  • Summarize
    Summarized using AI
    This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.

    Summary of Example- Schedule Optimization

    This example demonstrates how ServiceNow admins can configure the Schedule Optimization engine to efficiently schedule tasks for agents. It highlights three configuration methods: running batch optimizations overnight for large task volumes, executing intraday optimizations at selected intervals based on events, and enabling dispatchers to trigger on-demand optimizations directly from the Dispatcher Workspace. The goal is to maximize task assignments while minimizing travel time during agent shifts.

    Show full answer Show less

    Key Features

    • Batch Optimization: Configured to run overnight weekly batches (e.g., from 10 PM to 3 AM) to schedule a large number of tasks over a defined assignment horizon.
    • Intraday Optimization: Runs during business hours (e.g., 9 AM to 5 PM) to adjust schedules throughout the day based on current task and agent status.
    • On-Demand Optimization: Allows dispatchers to manually initiate scheduling optimizations in real time via Dispatcher Workspace for immediate adjustments.
    • Policy Configuration: Policies are set to maximize assignments, minimize travel time, and prioritize earlier shifts with equal weighting.
    • Scheduling Attributes: Includes travel time estimation using Beans.ai and specifies task states eligible for scheduling (Pending dispatch or Scheduled).
    • Scope and Assignment Groups: Defines geographic scopes (e.g., West Coast regions) and assignment groups (San Diego North and South) for targeted scheduling.

    Key Outcomes

    • Admins can balance workload and travel efficiency by applying policies that prioritize maximizing assignments and minimizing travel time.
    • Batch scheduling enables handling large task volumes efficiently during off-hours, improving resource utilization.
    • Intraday and on-demand scheduling provide flexibility to react to dynamic operational changes during the workday.
    • Dispatchers gain control to initiate immediate optimizations, ensuring agents' schedules remain optimal as conditions change.
    • The configuration tables serve as practical references for setting scheduling resolutions, batch timings, policies, and enabling on-demand features per assignment group.

    This example shows three different ways admins can configure the optimization engine to schedule tasks.

    Admins can configure Schedule Optimization to run overnight in batches to schedule a larger number of tasks or throughout the day at selected intervals based on events. Admins can also enable dispatchers to initiate Schedule Optimization from Dispatcher Workspace by configuring on-demand optimization.

    In this example, the organization is ensuring that agents complete as many tasks as they can during their shift without spending a lot of time traveling between tasks. A policy is configured to maximize assignments and minimize travel time. On-demand optimization is enabled for the dispatchers who are assigned to this group of agents.

    Admin Core Configurations for Schedule Optimization

    Table 1. Schedule Optimization Properties
    Field Value
    Qualifier type for Schedule Optimization Assignment Group
    Number of seconds used for task scheduling resolution 1
    Maximum number of location points allowed in a map provider call 300
    Table 2. Policies
    Field Value
    Name Maximum Assignments
    Active true
    Constraints Default values
    Overall objectives

    Maximize travel time (weight 1)

    Maximize task assignments (weight 1)

    Maximize assignments to earlier shifts (weight 1)

    Table 3. Scheduling Attributes
    Field Value
    Name West coast config
    Active True
    Travel estimate provider Beans.ai
    Default policy Maximum Assignments
    Straight line estimate config West Coast
    Tasks State is one of: Pending dispatch or Scheduled
    On Demand applicable policy West Coast Dispatcher

    Batch Optimization Configurations

    Table 4. Batch
    Field Value
    Name West Coast weekly
    Schedule start date 2023-12-01
    Run frequency Every 7 days
    Batch start time 22:00
    Batch end time 3:00
    Table 5. Scope
    Field Value
    Name West Coast-Next 7 days
    Active True
    Scheduling attribute configuration West Coast config
    Rank 1
    Assignment horizon offset 00
    Assignment horizon range Days 7
    Optimization Batch West Coast weekly
    Start date 2023-12-01
    Batch start time 22:00
    Batch end time 3:00
    Assignment group San Diego North
    Note:
    Select schedule now when the form is complete

    Intraday Optimization Configurations

    Table 6. Intraday Configurations
    Field Value
    Name West Coast
    Active True
    Default scheduling attribute configuration West Coast config
    Default False
    Flow Schedule intraday jobs (default)
    Default processing window Workday 9:00-5:00
    Assignment group

    San Diego South - Enable On Demand = True

    San Diego North - Enable On Demand = True

    On-demand Optimization configurations

    Table 7. On-demand values in Scheduling Attributes configuration
    Field Value
    On Demand applicable policy West Coast Dispatcher
    Table 8. On-demand values in Intraday configurations
    Field Value
    Assignment group

    San Diego South - Enable On Demand = True

    San Diego North - Enable On Demand = True